az_data <- az_counties |>left_join( pop_data, by =c("NAME"="county") )rdbu_palette <-rev(brewer.pal(5, "RdBu"))ggplot(data = az_data) +geom_sf(aes(fill = total_pop_change_20_23), color ="white") +scale_fill_gradientn(colors = rdbu_palette,name ="Population change",labels =function(x) format(x, big.mark =",")) +coord_sf() +labs(title ="Resident Population Change for Counties in AZ",subtitle ="July 01, 2020 to July 01, 2023",caption ="Source: Shapefile obtained using {tigris} R package, v2.2.1\npopulation change data from the US Census Bureau",x ="Longitude",y ="Latitude") +theme(plot.title.position ="plot")
ggplot(az_counties) +geom_sf(fill ='grey90', color ="white") +geom_sf(data = tribal_data, linewidth =1, fill =NA, color ="black") +geom_label_repel(data = tribal_data |>filter(NAME %in%c("Hopi Tribe", "Navajo Nation", "White Mountain Apache Tribe", "San Carlos Apache Tribe", "Tohono O’odham Nation")),aes(x = x, y = y, label = NAME),size =4,min.segment.length =0.1,box.padding =0.5,segment.color ="grey20") +coord_sf() +labs(title ="Indigenous Tribal Boundaries in AZ",caption ="Source: Shapefile obtained using {tigris} R package, v2.2.1\nIndigenous Tribe Shapefile obtained from AZGeo Data",x ="Longitude",y ="Latitude") +theme(plot.title.position ="plot")
Resources
Found help with reading a shapefile using the st_read function (Holtz 2023). Found help for using st_transform for converting coordinate systems (Heiss 2023).
main_plot <-ggplot(data = az_data) +geom_sf(aes(fill = total_pop_change_20_23), color ="white" ) +scale_fill_gradientn(colors = rdbu_palette,name ="Population change",labels =function(x) format(x, big.mark =","),guide =guide_colorbar(barwidth =9,barheight =1,direction ="horizontal",title.position ="top") ) +geom_rect(aes(xmin =-113.5, xmax =-110, ymin =31.25, ymax =34.25),fill =NA, color ="black", linetype ="dashed", linewidth =0.5 ) +geom_segment(data =data.frame(x =c(-113.5, -110),y =c(34.25, 31.25),xend =c(-122, -116.75),yend =c(32.75, 28)),aes(x = x, y = y, xend = xend, yend = yend),color ="black", linetype ="dashed", linewidth =0.5 ) +geom_label_repel(data =filter(az_counties, name %in%c("Maricopa", "Pinal", "Pima")),aes(x = x, y = y, label = NAME),size =4,min.segment.length =0.1,box.padding =0.5,segment.color ="grey20" ) +coord_sf(xlim =c(-122, -109), ylim =c(28.5, 37) ) +labs(title ="Resident Population Change for Counties in AZ",subtitle ="July 01, 2020 to July 01, 2023",caption ="Source: Shapefile obtained using {tigris} R package, v2.2.1\npopulation change data from the US Census Bureau\nIndigenous Tribe Shapefile obtained from AZGeo Data",x ="Longitude",y ="Latitude" ) +theme(legend.position =c(0.0, 0.7),legend.justification =c(0, 0),plot.title.position ="plot" )
zoom_plot <-ggplot() +geom_sf(data =filter(az_data, name %in%c("Maricopa", "Pinal", "Pima", "Santa Cruz", "Gila", "Yavapai")), aes(fill = total_pop_change_20_23), color ="white" ) +geom_sf(data = tribal_data, fill =NA, color ="black", linewidth =0.75 ) +scale_fill_gradientn(colors = rdbu_palette,name =NULL,labels =NULL,limits =range(az_data$total_pop_change_20_23,na.rm =TRUE) ) +geom_label_repel(data =filter(tribal_data, NAME %in%c("White Mountain Apache Tribe","San Carlos Apache Tribe","Tohono O’odham Nation")),aes(x = x, y = y, label = NAME),size =3, box.padding =0.5, min.segment.length =0 ) +coord_sf(xlim =c(-113.5, -110), ylim =c(31.25, 34.25) ) +theme_void() +theme(panel.background =element_rect(fill ="grey50"),legend.position ="none" )
final_map <- main_plot +inset_element( zoom_plot, left =0.0, bottom =0.0, right =0.5, top =0.5 )final_map
Resources
Found help with the patchwork inset_element(Pedersen 2024). Found help with drawing the line dashed line segments for geom_segment to emphasize the zoom window (Wickham 2024). Found help with drawing the dashed line box around the zoomed portion on the main plot using geom_rect(GeeksforGeeks 2023). I read through a few resources helping my get an idea of how to even approach a zoom window,, this source was quite instructive (Pebesma 2018).
